December and January were extremely unkind to me. I took a huge loss on December 9 that coincided with the Fed meeting and another big punch in January. In total, I went from a 28% profit to a ~4% net loss.

Deservedly, my inbox quickly flooded with comments and suggestions on the drawdown. The most common of those was to stop trading during news events.

So… why am I still trading during news events? There are a few answers to that question.

Curve fitting

It’s not like the strategy loses money on every single news event. It’s 100% true that news events like the Fed meeting can and badly hurt. Say that I’m determined to exclude news events in the future. I’d have to

Collect historical news event data

Create a second algorithm, which selects the news events that forbid and allow trading to continue

Test how the news algorithm interacts with Dominari

Repeat this many times until I’m happy with the final result

Due to the tiny number of news events that impact the markets like the December 9th announcement, my data set is miniature. The risk of overfitting to historical news events is huge.

Working with tiny amounts of data provides little in the way of long run confidence. Focusing my efforts elsewhere is far more likely to improve performance and requires much less work.

Too many trades

Too many trades sounds a bit naive, so let’s dig into what that means. Dominari trades a portfolio of 7 different instruments. All instruments cross with USD.

EURUSD

GBPUSD

USDCHF

AUDUSD

NZDUSD

USDJPY

USDCAD

Many subscribers correctly observed that the major losses occurred with trades open on all 7 pairs in the portfolio at the same time. A good predictor of trade performance is the number of trades open simultaneously.

Testing and confirming the max open trades rule was quick and easy. 5+ trades is very dangerous.

Accordingly, Dominari now exits all open trades if there are 5 or more trades open at any given time.

The next feature of Dominari will be a reversal strategy. Dominari was clearly prone to sudden equity changes if 5+ trades were open at the same time.

Make the losses work for us

An obvious counter strategy is to open trades in the opposite direction whenever Dominari would otherwise open too many trades. Testing the idea is very easy.

Coding a Dominari reversal strategy, however, would require a major reprogramming of the expert advisor’s code.

The number of trades per year would be miniscule. I doubt that it would average even 1 trade per month.

The idea is that Dominari can be the normal trading strategy. Whenever Dominari opens too many trades, the strategy then switches into reversal mode and trend trades with a simple trailing stop.

Switching direction should mostly reverse the negative trade skewness back in the positive direction. Almost all of the offending trades open at exactly the same time.

If the biggest losing trades opened at different times, there would be the risk of being too late to the party. All blowout trades opening at the same time means that the strategy can realistically reverse 100% of would-be losses into profits.

Sitting at the top of the docket are changes to Pilum. You can expect to hear about those soon so that I can incorporate Pilum into the Dominari signals. Once that and 2 other internal projects are finished, I’ll be able to dedicate the time required to fully implement the Dominari Reversal System.

Equity stop loss

Dominari uses emergency stop losses on all tickets. That is appropriate 99% of the time for individual trades. Those emergency losses reset once per hour in line with the concept of the TODS.

A little of the problem was bad luck. My stops came within a handful of pips of being triggered. Then they reset even further away, which made a bad problem worse.

When all trades move at the same time, then clearly the strategy could suffer extreme losses.

The first attempted solution after the Fed announcement was to add a portfolio level stop loss. The way that I wrote it also updated once per hour. When a second negative movement came in January, I stopped trying to be clever. It’s a flat, simple, stupid stop loss. If I lose more than 4% on all open trades, the entire Dominari portfolio goes flat.

I’m still trading Dominari

I still have my money trading the Dominari system; my confidence in the long term performance hasn’t changed, but it obviously requires safeguards. The max number of trades and the portfolio level stop loss will go a long way to limiting the impact of big moves in the future. AND, I should get the counter-strategy developed relatively soon to turn potential frowns upside down.

Lastly, many of you questioned why I’ve been so quiet. The honest answer is that I needed some time to process what happened. It’s easy to feel overwhelmed and discouraged when you get knocked down. I needed some time to process what happened.

I also needed time to double check the changes that I made to the portfolio were actually beneficial. It’s very easy to appease traders when they’re upset by rushing out features before they’re thoughtfully considered.

My money is on the line (I lost 2,000 euros between the two moves). What hurt my subscribers hurt me, too.

This post was authored by Ben Fulloon, a respected trader and subscriber to OneStepRemoved.

I developed an awesome strategy with a drawdown ratio of 13.67. Sounds amazing, right? Too bad that my trading platform overstated the results by more than double!

It’s important to learn about both your brokers and platforms limitations. Sometimes these intricacies only become apparent through time and experience. It’s so frustrating when your trading platform doesn’t function or report results as expected.

In this article I’ll point out two limitations of NinjaTrader 7, one bad limitation and one which can actually turn out surprisingly better for the trader in certain situations. However, this is more to do with the broker I’m using and not the platform itself.

NinjaTrader is definitely not the only platform that has limitations: MetaTrader, TradeStation, X-Trader, Matlab, etc. all have limitations for quantitative finance.

I’ll just be writing about NinjaTrader in this article to keep it fairly short and easy to read. I am also not intending to make out NinjaTrader as being a bad platform either. But, there are definitely some improvements that could be made to make it a lot easier and more convenient for quantitative traders to develop and trade strategies.

The first quirk relates to the broker I’m using. Specifically, it’s the day trade margins that I care about. These day trade margins end 15 minutes before the close of the session. For instance the ES (Emini S&P500) has a day trade margin of $500, which ends at 4:00pm CT that then reverts back to the full trading margin of $5060 before the session closes at 4:15pm CT. (Times stated are correct at time of Writing, The ES now closes at 4:00pm CT and the Day Trade margin ends at 3:45pm CT)

I’ll show you a screenshot of the results of a day trading strategy that I developed. This strategy trades the ES, NQ (Emini Nasdaq 100) and the YM (Emini Dow) all at the same time. The easiest way to exit on close with NinjaTrader is setting “Exit on Close” to true which will then exit on the close of the session.

According to the results the strategy makes a total of $332,771.60 with a maximum drawdown of $25,912.27 since 2008 to now. This is a drawdown ratio of 12.84. That’s oustanding!

The issue is… and you knew there’d be a problem… is that the strategy exits at 4:15pm CT. Day trading margin ends at 4:00pm CT. The strategy is therefore highly likely to get a margin call with a small account size.

It makes sense to tweak the strategy to make best use of the day trading margin. Ninjatrader offers a custom session template, which in this case I made end at 4:00pm CT. The results of the custom session template is as follows.

The exact same strategy applied to the same instruments to avoid a margin call makes $335,819.30 with a maximum drawdown of $24,560.51. This is a drawdown ratio of 13.67.

I didn’t change the strategy with the goal of improving the drawdown ratio AND the profit. But hey, I’ll take it. Finding a limitation in the platform can actually benefit you in some situations.

This strategy is based on trading 3 different instruments. The ES, the NQ and the YM. The problem is that I backtested it using an instrument list in NinjaTrader. What this means is they’re all tested separately. NinjaTrader then combines the test results for you as a total result like the results of the screenshots above.

Here’s what it looks like when you test them as an instrument list. This shows the different profits and drawdowns of the individual instruments.

Now at first glance it reads that the trader would have made $335,819.30 with a maximum drawdown of $24,560.51 if they traded all three instruments together. Don’t you agree?

The problem is that this is incorrect. NinjaTrader doesn’t actually combine the results like you’d think. The trader still would have made roughly that money. However, all the statistics aren’t quite correct.

To show this I recreated the exact same strategy however it will trade the ES, NQ and YM all at the same time instead of trading them separately like it does by default. These are the results when you program it into a multi-instrument strategy

It makes $335,915.30 which is roughly the same amount, but it has a maximum drawdown of $59,937.60 instead of the $24,560.51 it originally looked like it would be. This makes it a drawdown ratio of 5.60, which is a lot worse than the original 13.67.

If the trader decided to trade based upon the maximum drawdown of $24,560.51, they may get a nasty shock when the drawdown turns out to be twice as bad as they were expecting.

Incorrect calculations on such an important metric could jeopardize an account. You might assume that you can get away with half of the equity that’s actually required to trade the strategy. Oops?!?

The misleading statistics in NinjaTrader makes this strategy look really nice. But when the drawdown is more than double what it appeared that it would have been originally, you might get a nasty shock.

This is why it’s important to learn both your platforms and brokers limitations as early as possible. You don’t want to learn these limitations the hard way.

In a few weeks time, I’ll reveal a simple way to create multi-instrument strategies which show more accurate metrics. Stay tuned for my next article in the series.

There is clearly something going on with the current market conditions. Literally every currency pair is blowing out into a single direction. The addition of the recent pairs hasn’t done anything to stop it.

Rather than trying to fight this and hope and hope, it’s best to take a time out and see where the chips fall. I did spot a few issues with my long term trend direction that would have made this pain less bad – there is at least a small improvement that will come out of this.

That said, the mantra for now is live to fight another day. It’s clear that the market conditions are beating the snot out QB Pro. I’m going to make the hard decision, which is to go flat and do nothing. This is exactly the reason that I don’t charge management fees – I’d feel awful trying to charge people for this recent performance.

I’ll update everyone in a week or two when we’re ready to evaluate whether or not to flip the switch back on.

It’s been a bumpy month by any definition. We made a ton of money in the aftermath of last month’s Fed announcement, only to give it all back the next week. QB Pro recovered most of the earlier gains, then last week’s drawdown took it all back again. It’s been painful.

The good news is that the new changes to QB Pro are rolled out. Several of you sent in emails asking about new currencies like GBPNZD and AUDCAD appearing in your account. Kudos to you for paying close attention to the trading.

The total currencies traded in the basket is up to 16 pairs. While the max leverage is unchanged at 36:1 (still very, very high), the leverage per pair is only 2.25:1. Future losses like the one from last week will still occur.

The difference is that the size of the positions is reduced by over 2/3. The impact of getting caught in losing trades that are all reflective of USD weakness decreases significantly. We’re now trading a mix of AUD, CAD, CHF, EUR, GBP, JPY, NZD, USD and XAG. No one currency should dominate the performance.

The system also does extremely well on emerging market currencies. I’m holding off on adding RUB, MXN and others until I determine the impact of the spreads on overall profitability. They’d do amazing if we could trade for free!

Short term performance expectations for QB Pro

We’re coming into the summer, which is when the forex market traditionally falls into the doldrums. That’s generally a good thing for QB Pro. The markets whipsaw up and down without really going anywhere.

The alternative is that the Fed hikes rates in June and sends the market into a USD buying frenzy. That’s also good news. Most of the money that QB Pro made over the past 8 months was driven by USD strength. A rate hike would unleash chaos in emerging markets and equities. That’s the kind of condition to push volatility into our new crosses, creating opportunities for us to trade.

QB Pro 2.0 isn’t happening

I’m extremely disappointed. After several thousand dollars in programming expenses, and not to mention the 100+ hours that I spent coding myself, the QB Pro 2.0 change is a wash.

I had a trusted developer audit my code to make sure I wasn’t doing something stupid like trading on future prices or anything. Neither him nor myself caught anything from December until March.

Towards the end of last month, a single line of code ruined it all. One of my key features was deciding when to bail on trades and go the opposite direction. Well, it turned out that I accidentally introduced data snooping into the backtesting platform. I pre-calculated when losing trades occurred to calculate probabilities.

In plain English, my goal was to calculate “If today was a big loser, then do the opposite tomorrow.”

What I accidentally coded was “If tomorrow is a big loser, then do the opposite.” If only that were possible!

I don’t want to muddle up the explanation with code examples. Suffice it to say that the idea didn’t work out when I took away the ability to look into the future.

There are some features of the 2.0 system that I wish to analyze in the coming months, but for now it’s going to have to take a back seat.

What’s next?

My plan is to sit tight for a few weeks to ensure that the new pairs are working as intended. Whenever I am personally satisfied with the system behavior, I intend to increase the amount of capital in my account.

Don’t hold my feet to the fire. This part is a subjective process, so I can’t put a precise time frame on it. If and when I am satisfied – and it’s going very well the first few days – then I will make a decision about increasing my capital at risk.

If and when I choose to increase my capital in the account, I will then re-open QB Pro to new traders.

PS: I hope that the drawdowns encourage some of you to withdraw profits the next time the opportunity presents itself. You don’t want to lose more than you are comfortable risking.

The gains can accumulate quickly when a prop trader is using a strategy based on maximum leverage with limited account size. In order to preserve and build those gains, it’s important to remove them from the trading account according to a good plan.

As described in previous articles in this series, the high-leverage, low-balance strategies used by leading prop traders can be applied to multiple trading accounts using different systems, with each account capitalized by not more than a couple thousand dollars.

The amount in the account typically ranges between $1,000 to several thousand dollars. That way, there’s no psychological obstacle to using the max leverage on each trade.

Reduce the risks from drawdowns

When you have a winning system, profits pile up. It’s tempting to “let it ride” by using the same system to trade ever-bigger position sizes in the growing account.

However, when the entire capital is available in the trading account, it means that the capital is exposed to the inevitable system “blow up,” which typically causes a steep drawdown. Even if the trader escapes financial catastrophe, he or she may become so risk-averse afterward as to become indecisive and ineffective.

Pull money out each month

The smart way to avoid excessive drawdowns due to trading system “blow ups” is to pull money out of the account at the end of each successful month. That way, when a major drawdown occurs, it won’t take all your money, just the couple thousand dollars that you can afford to lose.

Successful prop traders like Shaun sweep the profits out of each winning trading account monthly and move them into a non-trading account, where they remain safe. So, each month the trading accounts open with their individual capitalization set at a given amount.

Pull out at least enough to cover one “blow up”

Once you’ve launched your forex system, you’ll want to think about earmarking enough money to cover at least one trading system failure. After you’ve secured that amount to be used for a recapitalization of your trading account, every subsequent gain is “free money,” at least in a psychological sense.

The first milestone is to pull enough money out of the trading account to cover at least one catastrophe. If you’ve been enjoying mostly winning months, next you should allocate 50% of your profits for high-risk systems.

You can’t lose what’s not at risk

Keep in mind: When a prop trader is using maximum leverage, the only money that’s safe is the money already pulled out of the trading account. Profits should be pulled from each winning trading account, each month.

When a prop trader wins consistently using high leverage with a limited-size account, the gains from relatively small individual trades may compound quickly. Profits gathered from the overflowing small trading accounts can compound into large sums, and it’s important to manage those profits effectively.

If you’d like to learn more about using maximum leverage to pull profits each month, just contact Shaun.

One of the questions that every quantitative trader must address is whether adding a stop-loss component to their system will help or hinder its performance. I have written a good deal lately about the pros and cons of different types of stops, but haven’t had much actual backtesting data to work with.

When I wrote a post about about Cesar Alvarez’s S&P Rotational Strategy a few weeks ago, I suggested that adding a stop-loss might lower the maximum drawdowns. This would give the strategy a way to exit losing positions during the month, rather than waiting for the monthly redistribution. Theoretically, this would have reduced some of the big losses that the strategy suffered in 2008.

We assume that adding a stop loss component has the quantitative value of a safety net, but that isn’t always the case.

In addition to writing about that idea here, I also commented on Alvarez’s post. In response to that, he has written a follow-up post addressing my suggestion to implement stops:

Continuing from the post, we are adding a maximum stop loss. The stop is evaluated at the close each day with the exit happening at the close. The tested stops are 5%, 10% and 15%.

Interestingly, Alvarez finds that adding stops can be helpful in some situations and terrible for performance in other situations. While adding stops may always seem like a logical idea in theory, Alvarez shows that actual performance can prove otherwise.

Best Performing Stocks

The version of the best performing stocks strategy that we looked at in the previous post utilized a market timing indicator and a six month look-back period. That strategy produced a CAR of 10.48% with a maximum drawdown of 42.22%. Here are the numbers when 5, 10, and 15 percent stops were added:

5% Stop: 10.51% CAR, 26.30% MDD

10% Stop: 10.85% CAR, 38.05% MDD

15% Stop: 10.84% CAR, 39.48% MDD

As you can see, adding the stop loss doesn’t do much for the CAR, but it does a great job of reducing the maximum drawdown. When the stops were applied to the version of the strategy with a 12 month look-back period, the impact on maximum drawdown was similar, but the CAR saw a bit more of an increase. When the stops were applied to each of the two versions without the market timing indicator, we saw a slightly less impact on drawdown and a much greater impact on CAR.

Alvarez also commented that in almost all cases, the 5% stop was the best performer, which he thought was unusual:

Normally close stops tend to be the worst but the 5% stop tends to be the best.

Worst Performing Stocks

The worst performing stocks version of the strategy that we looked at used the market timing indicator and a six month look-back period. The strategy without stops had a CAR of 13.05% and a maximum drawdown of 27.88%. Here are what the numbers look like when the different levels of stops were applied:

5% Stop: 5.11% CAR, 28.26% MDD

10% Stop: 8.36 CAR, 30.90% MDD

15% Stop: 10.47% CAR, 30.87% MDD

In this case, adding the stops has really hurt the strategy. While there was some improvement in the maximum drawdowns of some of the versions, adding stops basically crippled the CAR of all of the worst performing stocks strategies.

Alvarez notes that this is the result he expected:

For the worst N-month ranking, stops appear to hurt the all results. These results support previous research that stops on short-term mean reversion hurt results.

Daniel Fernandez posts a nice summary of some of the problems algorithmic traders have experienced over the past few years. If you’ve been wondering why your expert advisor isn’t making money, well, you’re not alone.

Daniel points out the terrible performance of the Barclays systematic trading index and its nearly three years of continuous losses. Even the pros are losing money consistently.

The performance of professional systems traders has fallen over the past two years

Key sections:

It is no secret that algorithmic trading had some “golden years” between 2008-2011. Through this period – most notably due to the high directional volatility of the financial crisis – systems based on a wide variety of market characteristics were able to obtain high amounts of profit, with an almost completely negative correlation with equity markets. Among the high-performers found during this period, trend followers were perhaps the most impressive, with some systems achieving returns of more than 100% of capital within this period, with little drawdown whatsoever. During these years everyone trading algorithms was making a killing. Then, change happened.

The answer seems to be simple and at the same time incredibly complex: fundamental influence and uncertainty. Algorithmic trading systems are all designed with the idea that some historical assumption will continue to be true in the future. This assumption can be that price tends to break at a certain hour, that momentum created in one direction leads to continuations, that two instruments are co-integrated, etc. When these assumptions break, the algorithms fail because they have no way to know that under current market conditions their assumptions are no longer valid. This “breaking up” of algorithms means that we usually need to take loses to realize that something has changed – to remove or modify our strategy – and this makes us invariably less reactive than human traders. The strength of algorithmic trading, it’s high capacity to exploit structural characteristics, becomes its weakness when the underlying structure changes.

Last week, I wrote a post discussing how altering the timeframe of a system can change its results. That got me thinking about other ways that backtesting results could be skewed in one way or another based on user defined data such as the date range and market used. These simple differences can have a tremendous influence on the overall returns of any system, so it is important to pay them their proper respect.

When running backtests, it can be very easy to gloss over the down periods and cherry-pick the big return years. The problem is that you won’t have that opportunity when actually trading a system live. You will need to prepare yourself for the possibility that you select the wrong time or the wrong market to trade a given system. Otherwise, you run the risk of letting these backtesting biases adjust your expected return to values the system cannot possibly deliver.

Adjusting the Date Range

Let’s use our 10/100 Moving Average Crossover System from last week as a base. We tested it from January 1, 2001 through December 31, 2010 on the Vanguard Total Stock Market ETF (VTI). All of our tests last week used a starting portfolio value of $10,000, a 10% trailing stop, and a $7 commission.

MA crosses on VTI returned almost 90% over the last decade.

Based on those settings, our 10/100 MA Crossover System returned 89.8% over ten years. This works out to be an annualized return of 12% with a maximum drawdown of 16.2%.

If we would have started trading this system on January 1, 2003, we would have registered a total return of 39% in the three years of trading until the end of 2005. This would have been good for a 16.4% annualized return with a maximum drawdown of only 6%. As you can see, if we based our strategy on these results, we would be expecting the system to continue to produce these extremely high returns.

On the other hand, if we would have started trading this system on January 1, 2006, we would have seen a total return of only 2.5% in the first three years. We also would have had to sit through a 14.2% drawdown.

It is also worth noting that while the ten year track record of this system from 2001 through 2010 is very respectable, we wouldn’t have known that when we started in 2001. If we actually started trading this system in 2001, we would show a total return of -6.2% at the end of 2002. After two full years trading this system, we would not have had a single thing to show for it. The system didn’t find its first big winner until April 15, 2003.

As you can see, the time you chose to begin trading the 10/100 Moving Average Crossover System could have made all the difference over the course of what was a net-profitable decade. It is very important to keep this in mind when you are struggling through drawdowns.

Adjusting the Markets Traded

The market you choose to trade can have the same affect on your trading as the date you start trading. Let’s look at how the exact same system would have performed over the exact same decade if we chose to trade it on different ETFs.

Trading the 10/100 Moving Average Crossover System on the XLF, which represents financials, would have provided a total return of -9.4% for the decade with a maximum drawdown of 30%. It is obvious to us at this point that financials had a rough time during this period, but we would have had no clue about that when we started in 2001.

The XLY, which represents consumer discretionary stocks, also would have underperformed the VTI. Trading the system on the XLY would have returned a total of 39.4, or 6.4% annually, with a maximum drawdown of 21.3%.

XLY shows a 39.4% return over the same decade

If we would have been fortunate enough to trade the XLK, which represents the technology sector, we would have seen a tremendous total return of 95.7%. This works out to be an annual return of 14.2% with a maximum drawdown of 22.3%.

Once again, we see that decisions like what markets to trade and when to start can have a tremendous influence on our results. This is why it is so important to thoroughly backtest any strategy across many different combinations of date ranges and markets.

At the root of most trend following systems is the idea that you want to be long markets that are moving higher or short markets that are moving lower. One of the simplest and most popular ways to identify markets that are trending up or down is to look for ones that are making new 52-week highs or lows. This simple signal can be used as the root of a very simple and easy to follow trend following system.

About The System

In his book, Unholy Grails, author and trader Nick Radge discusses a number of different systems providing set rules and backtesting results. One of the first systems he describes is based on the concept that stocks making new yearly highs are likely to continue higher, and stocks making new yearly lows are likely to continue trending lower. This is one of the core concepts behind most systematic trend following strategies.

Radge points out that a system that is based on new yearly highs and lows is very user-friendly because most financial resources publish data for stocks making new 52-week highs and lows. This means that we could literally trade this system manually without any trading software.

One way that Radge simplifies the system is by assuming that there are approximately 250 trading days in a year. Therefore, any stock making a new 250-day high or low will also be making a new yearly high or low. This means we can treat this system as a very simple 250-day breakout system.

This system establishes a long position on the open the day after a stock makes a new 250-day high and then exits that position on the open of the day after the stock makes a new 52-week low. These 250-day highs and lows are easy to monitor using a price channels overlay, which is available in most charting packages. The solid yellow lines on the chart below represent the 250-day high and low prices.

BND forms a new 250 day low

Trading Rules

Enter Long When:

Price Closes At New 250-Day High

Exit Long When:

Price Closes At New 250-Day Low

Backtesting Data

In order to backtest this system, Radge used the stocks in the All Ordinaries Index, which is the most popular stock market index in Australia. He compares his results to the All Ordinaries Accumulation Index, which is a benchmark that represents a buy and hold approach on the Australian equities markets.

His testing period ran from January 1, 1997 through Jun 30, 2011. The account started with $100,000 and sized positions at 5% of the account. Commissions and dividends were also taken into account.

Trading the New Yearly High System over those 14 years would have produced a total of 170 trades. The Compound Annual Growth Rate (CAGR) would have been an impressive 18.21%, more than doubling the 8.78% CAGR of the benchmark buy and hold account. The system was profitable on 53.3% of its trades and posted a Sharpe Ratio of 0.395.

The glaring negative of the backtesting results would be that the New Yearly High System posted a maximum drawdown of over 50%.

System Analysis

One of the most common things you will hear about system development is that traders place far too much emphasis on entry and exit signals. Conversely, there is generally not enough emphasis placed on risk management and position sizing. This system is a great example for those philosophies because its entry and exit signals appear to work just fine, but its 50% drawdown indicates that it doesn’t do a good enough job protecting its profits.

The New Yearly Highs System has a significant strength in that it works. Its returns actually beat a buy and hold approach by more than double. However, its weakness needs to be addressed, because it would be an absolutely nightmare to attempt to stick to the system while sitting through a 50% drawdown.

Improving The System

Trend Filter

The first thing I would do to improve this system is introduce a trend filter using the 200 day simple moving average (SMA). This filter would simply require that the general market be in an uptrend in order for the system to enter a long position in any single stock. This would ensure that the system was not taking long positions in stocks that were trying to trend higher against a downtrending general market. We should have more overall success if we consistently trade with the trend of the overall market.

Trailing Stop

Another condition I would add to the system would be an ATR-based trailing stop that would ensure that we locked in profit from any profitable trades. It would also minimize our losses on losing trades. Adding this stop might cut down the overall returns a bit by cutting short trades that would have eventually turned profitable, but the downside protection would likely be worth that sacrifice.

Changing the Universe

One of the things that Radge suggests is trading the system exclusively on the market index as opposed to individual stocks. He suggests that this would cut down on volatility and then proves that concept with backtesting results. This idea lowered the CAGR to 13.92%, but also cut the maximum drawdown down to 36.03%.

Keeping with the line of thinking that trading the system on indexes would reduce volatility, I would be curious to test the system trading across multiple indexes or ETFs. It would be interesting to see how the system performed on the ETFs traded by the Ivy Ten System or the markets recommended by Andreas Clenow in Following The Trend.

After deciding to explore system trading, many traders are tempted to expedite the process by purchasing someone else’s system. These traders are often met with disastrous results.

In order to successfully trade a system, you must have unshakable confidence in that system and the system must fit your personality. The only way to achieve this is to build your own system.

Confidence In Your System

No trading system is perfect. All trading systems experience drawdowns. The difficult part comes when you try to determine if a drawdown is normal or if markets have fundamentally changed in a way that erases your system’s edge. The only way that you will be able to tell the difference is if you know your system inside and out. That only happens when you build it yourself.

There are many trading systems that you can purchase for a wide range of prices. Some are based on solid strategies. Some are over fitted to a specific style of market. The problem is that if you jump into using someone else’s system, you won’t know how it was designed to handle different markets. When the system experiences a drawdown – it’s going to happen – you won’t know whether or not that drawdown is normal.

Shaun wrote a post earlier this year discussing drawdowns. He referenced how Dustin Pedroia struggled when the Boston Red Sox first called him up to the big leagues. The Red Sox stuck with their young second baseman because they were able to identify that his low average was not sustainable because of his high contact rate.

Shaun compared the Red Sox sticking with Pedroia to a trader sticking with his system during a drawdown. The Red Sox were able to understand Pedroia’s slump because they were able to look deeper into his performance statistics. A system trader can do the same thing if he knows his system well enough to look deeper into its performance statistics.

The Red Sox stuck with Pedroia during a slump because they knew how good he was

By taking the time to backtest a trading system through different market periods, you will gain an understanding of how the system reacts to different types of markets. For example, if your system creates most of its profits during a trending market, then you won’t have any reason to panic if it experiences a drawdown during a non-trending period. That would be expected. However if the same system was struggling to produce in a trending market, you would be more concerned.

Building A System The Fits Your Personality

Another reason that you must construct your own trading system is that the system needs to fit your personality. The system used by The Turtles is probably the most famous system of all time, and you can purchase software that trades that exact system for less than $1,000. The problem with that approach is that you might not be a good fit to trade the way the Turtles traded.

The amount of trades a system makes, the time frame that those trades are held, and amount of capital risked on each trade are all factors that affect how a system suits your personality. While the Turtle’s system worked for some traders, if that system exposes your capital to more risk than you are comfortable with, then you won’t be able to sleep at night. While each of these factors can be adjusted during the process of building a system, many black box systems do not allow for adjustments. Also, without backtesting data, you won’t be able to determine how these adjustments will affect the system’s performance.

Going back to Shaun’s Dustin Pedroia reference, the Boston Red Sox were able to have confidence in the numbers of their short, odd-looking prospect because he fit their personality. They also had great success when they acquired corner infielder Kevin Youkilis, who was not an outstanding hitter, but had an exceptional ability to draw walks. If either of these players made the front office feel uncomfortable, they never would have been successful.

Youkilis is a player that makes the Red Sox comforable. Are you comfortable with each element of your trading system?

Acquiring players like Pedroia and Youkilis fit the personality of the Boston Red Sox, who were obsessed with crunching numbers and didn’t mind looking foolish if they were wrong. I would compare this to trading a system that focused on absolute return that also expects steep drawdowns.

On the opposite end of the spectrum, the New York Yankees are known for acquiring players that are already proven commodities later in their careers for much higher salaries. This approach is more in line with a trading system that trades for smaller profits with much less risk.

OneStepRemoved.com is dedicated to helping traders find a system appropriate for their personalities. Email info@onestepremoved.com and let us know how we can help with your trading.